Due to the complex underwater imaging environment and illumination conditions, underwater images have some quality degradation problems, such as low contrast, color distortion, texture blur and uneven illumination, which seriously restrict the application in underwater work. In order to solve these problems, we proposed a multi-scale feature fusion CNN based on underwater imaging model in this paper called Multi-Scale Convolution Underwater Image Restoration Network (MSCUIR-Net). Unlike most previous models that estimated the background light and transmittance, respectively, our model unifies the two parameters into one, predicts the univariate linear physical model through lightweight CNN, and directly generates end-to-end clean images. Based on the underwater imaging model, we synthesized the underwater image training set can simulate the shallow water to deep water environment. Then, we do experiments on synthetic images and real underwater images, and prove the superiority of this method through image evaluation indexes. The experimental results show that MSCUIR-Net has a good effect on underwater image restoration.

由于水下成像环境和光照条件复杂，水下图像存在对比度低、色彩失真、纹理模糊、光照不均匀等质量退化问题，严重制约了水下工作的应用。为了解决这些问题，我们在本文中提出了一种基于水下成像模型的多尺度特征融合CNN，称为多尺度卷积水下图像恢复网络（MSCUIR-Net）。与之前大多数模型分别估计背景光和透射率不同，我们的模型将这两个参数合二为一，通过轻量级 CNN 预测单变量线性物理模型，并直接生成端到端的干净图像。基于水下成像模型，我们合成的水下图像训练集可以模拟浅水到深水环境。然后，我们对合成图像和真实水下图像进行了实验，并通过图像评价指标证明了该方法的优越性。实验结果表明MSCUIR-Net对水下图像恢复有很好的效果。